When GNSS signals travel from GNSS satellites to Earth, they pass through our atmosphere. As these layers change the speed and path of the radio waves, a slight delay occurs before the signals reach the receiver. By estimating the portion of this delay attributable to water vapor, we can exactly determine the total amount of water vapor in a column of air.
The amount of water vapor contained in a unit column of air from the Earth's surface to the top of the atmosphere is called 'Precipitable Water Vapor (PWV).' PWV derived from GNSS can be estimated with an accuracy almost equivalent to 'measured' PWV, which is obtained by vertically integrating the water vapor amount at each altitude using meteorological instruments (e.g., radiosonde). Today, meteorological agencies worldwide use GNSS PWV as one of the inputs to daily numerical weather prediction models.
One advantage of GNSS is that it can provide observations with a higher temporal resolution than PWV measurements from conventional meteorological instruments. From another perspective, because water vapor fluctuates across various spatial and temporal scales, existing observation networks sometimes lack sufficient resolution and fail to accurately capture these variations. To address this, our consortium is expanding the observation network by leveraging SoftBank's ultra-dense original network. This research aims to grasp localized water vapor fluctuations and rapid changes that were previously unobservable. For example, under conditions where cumulonimbus clouds develop rapidly, research is focusing on how water vapor fluctuates in space and time from their generation to decay. Also, in extreme weather events such as heavy rainfall or heavy snowfall, key research themes include the magnitude of observed precipitable water vapor—specifically the quantitative understanding of continuously inflowing water vapor and its regional characteristics (e.g., Fujita 2026).
The Japan Meteorological Agency website shows that observations indicate rainfall and snowfall events are becoming more extreme due to the progression of global warming (see URL below). Quantitative observation of water vapor, the very source of such rain and snow events, provides essential data for deepening our understanding of these phenomena. By clarifying water vapor fluctuations in detail at high temporal and horizontal resolutions, this work is expected to significantly improve weather forecasting accuracy for disaster prevention
※ https://www.data.jma.go.jp/cpdinfo/index_extreme.html
Reference：
Fujita, M, Capturing Extreme Water Vapor and Instability with High‐Resolution GNSS Monitoring. Atmospheric Science Letters, 27(2). https://doi.org/10.1002/asl2.70010, 2026.